Simulation-based design optimization for statistical power: Utilizing machine learning.

The planning of adequately powered research designs increasingly goes beyond determining a suitable sample size. More challenging scenarios demand simultaneous tuning of multiple design parameter dimensions and can only be addressed using Monte Carlo simulation if no analytical approach is available. In addition, cost considerations, for example, in terms of monetary costs, are a relevant target for optimization. In this context, optimal design parameters can imply a desired level of power at minimum cost or maximum power at a cost threshold. We introduce a surrogate modeling framework based on machine learning predictions to solve these optimization tasks. In a simulation study, we demonstrate the efficiency for a wide range of hypothesis testing scenarios with single- and multidimensional design parameters, including t tests, analysis of variance, item response theory models, multilevel models, and multiple imputations. Our framework provides an algorithmic solution for optimizing study designs when no analytic power analysis is available, handling multiple design dimensions and cost considerations. Our implementation is publicly available in the R package mlpwr. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Sample size planning for complex study designs: A tutorial for the mlpwr package.

A common challenge in designing empirical studies is determining an appropriate sample size. When more complex models are used, estimates of power can only be obtained using Monte Carlo simulations. In this tutorial, we introduce the R package mlpwr to perform simulation-based power analysis based on surrogate modeling. Surrogate modeling is a powerful tool in guiding the search for study design parameters that imply a desired power or meet a cost threshold (e.g., in terms of monetary cost). mlpwr can be used to search for the optimal allocation when there are multiple design parameters, e.g., when balancing the number of participants and the number of groups in multilevel modeling. At the same time, the approach can take into account the cost of each design parameter, and aims to find a cost-efficient design. We introduce the basic functionality of the package, which can be applied to a wide range of statistical models and study designs. Additionally, we provide two examples based on empirical studies for illustration: one for sample size planning when using an item response theory model, and one for assigning the number of participants and the number of countries for a study using multilevel modeling.

Power Analysis for the Wald, LR, Score, and Gradient Tests in a Marginal Maximum Likelihood Framework: Applications in IRT.

The Wald, likelihood ratio, score, and the recently proposed gradient statistics can be used to assess a broad range of hypotheses in item response theory models, for instance, to check the overall model fit or to detect differential item functioning. We introduce new methods for power analysis and sample size planning that can be applied when marginal maximum likelihood estimation is used. This allows the application to a variety of IRT models, which are commonly used in practice, e.g., in large-scale educational assessments. An analytical method utilizes the asymptotic distributions of the statistics under alternative hypotheses. We also provide a sampling-based approach for applications where the analytical approach is computationally infeasible. This can be the case with 20 or more items, since the computational load increases exponentially with the number of items. We performed extensive simulation studies in three practically relevant settings, i.e., testing a Rasch model against a 2PL model, testing for differential item functioning, and testing a partial credit model against a generalized partial credit model. The observed distributions of the test statistics and the power of the tests agreed well with the predictions by the proposed methods in sufficiently large samples. We provide an openly accessible R package that implements the methods for user-supplied hypotheses.

Characterisation and Comparison of Material Parameters of 3D-Printable Absorbing Materials.

We compared different commercially available materials that are 3D-printable for their suitability for making microwave absorbers by means of additive manufacturing, i.e., 3D printing. For this, we determined their complex permittivity, and, if applicable, the complex permeability. They are responsible for the RF losses within the material and, therefore, determine its usefulness as an absorber material. Further, we made SEM (scanning electron microscope) images of material samples showing the filling materials that have been used to achieve absorbing properties.

Conspiracy mentality and political orientation across 26 countries.

People differ in their general tendency to endorse conspiracy theories (that is, conspiracy mentality). Previous research yielded inconsistent findings on the relationship between conspiracy mentality and political orientation, showing a greater conspiracy mentality either among the political right (a linear relation) or amongst both the left and right extremes (a curvilinear relation). We revisited this relationship across two studies spanning 26 countries (combined N = 104,253) and found overall evidence for both linear and quadratic relations, albeit small and heterogeneous across countries. We also observed stronger support for conspiracy mentality among voters of opposition parties (that is, those deprived of political control). Nonetheless, the quadratic effect of political orientation remained significant when adjusting for political control deprivation. We conclude that conspiracy mentality is associated with extreme left- and especially extreme right-wing beliefs, and that this non-linear relation may be strengthened by, but is not reducible to, deprivation of political control.

Abstinence from Masturbation and Hypersexuality.

Despite the lack of evidence for negative health effects of masturbation, abstinence from masturbation is frequently recommended as a strategy to improve one's sexual self-regulation. We adopted a framework of perceived problems with pornography to collect first hints about whether abstinence from masturbation stems from a psychological and behavioral "addiction" or conflicting attitudes. In an online questionnaire survey recruited via a non-thematic Reddit thread (n = 1063), most participants reported that they had tried to be abstinent from masturbation. As visible from zero-order correlations and multiple linear regression, motivation for abstinence was mostly associated with attitudinal correlates, specifically the perception of masturbation as unhealthy. While there were associations with hypersexuality, no significant correlation with behavioral markers such as maximum number of orgasms was found. Higher abstinence motivation was related to a higher perceived impact of masturbation, conservatism, and religiosity and to lower trust in science. We argue that research on abstinence from masturbation can enrich the understanding of whether and how average frequencies of healthy behavior are pathologized.